Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations9974
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory518.5 B

Variable types

Text4
DateTime1
Numeric9
Categorical2

Alerts

energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with energyHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
time_signature is highly imbalanced (74.5%) Imbalance
song_id has unique values Unique
key has 1198 (12.0%) zeros Zeros
instrumentalness has 2102 (21.1%) zeros Zeros

Reproduction

Analysis started2024-12-17 03:22:02.277568
Analysis finished2024-12-17 03:22:13.343971
Duration11.07 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

song_id
Text

Unique 

Distinct9974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size769.6 KiB
2024-12-16T22:22:13.649946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters219428
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9974 ?
Unique (%)100.0%

Sample

1st row57Pk2GU0ABFYBbbcgYxqki
2nd row6WzRpISELf3YglGAh7TXcG
3rd row6z3HAUZpAyJ0ctsbAwAiw3
4th row6uFn47ACjqYkc0jADwEdj1
5th row4GssB27iJeqmfGxS94Tfij
ValueCountFrequency (%)
57pk2gu0abfybbbcgyxqki 1
 
< 0.1%
79s5xncn4tjktvmsmox8ep 1
 
< 0.1%
3cijedprufiku5tdxey7aw 1
 
< 0.1%
5zd9tgduwbffxwgnm3k3rz 1
 
< 0.1%
6z3hauzpayj0ctsbawaiw3 1
 
< 0.1%
6ufn47acjqykc0jadwedj1 1
 
< 0.1%
4gssb27ijeqmfgxs94tfij 1
 
< 0.1%
4myfsmx2v6zndojfn3ikbd 1
 
< 0.1%
6avihku3ydzaepbmzei62v 1
 
< 0.1%
3wn8noygkhluq9dlxm1rkw 1
 
< 0.1%
Other values (9964) 9964
99.9%
2024-12-16T22:22:13.940448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4758
 
2.2%
3 4703
 
2.1%
2 4676
 
2.1%
0 4673
 
2.1%
4 4621
 
2.1%
1 4595
 
2.1%
6 4558
 
2.1%
7 4402
 
2.0%
z 3521
 
1.6%
F 3477
 
1.6%
Other values (52) 175444
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 219428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 4758
 
2.2%
3 4703
 
2.1%
2 4676
 
2.1%
0 4673
 
2.1%
4 4621
 
2.1%
1 4595
 
2.1%
6 4558
 
2.1%
7 4402
 
2.0%
z 3521
 
1.6%
F 3477
 
1.6%
Other values (52) 175444
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 219428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 4758
 
2.2%
3 4703
 
2.1%
2 4676
 
2.1%
0 4673
 
2.1%
4 4621
 
2.1%
1 4595
 
2.1%
6 4558
 
2.1%
7 4402
 
2.0%
z 3521
 
1.6%
F 3477
 
1.6%
Other values (52) 175444
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 219428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 4758
 
2.2%
3 4703
 
2.1%
2 4676
 
2.1%
0 4673
 
2.1%
4 4621
 
2.1%
1 4595
 
2.1%
6 4558
 
2.1%
7 4402
 
2.0%
z 3521
 
1.6%
F 3477
 
1.6%
Other values (52) 175444
80.0%

title
Text

Distinct7816
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size771.6 KiB
2024-12-16T22:22:14.202549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length217
Median length100
Mean length19.813816
Min length1

Characters and Unicode

Total characters197623
Distinct characters306
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7019 ?
Unique (%)70.4%

Sample

1st rowPopular - From "Wicked" Original Broadway Cast Recording/2003
2nd rowPopular (with Playboi Carti & Madonna) - From The Idol Vol. 1 (Music from the HBO Original Series)
3rd rowPop Muzik
4th rowPop Out (feat. Lil Tjay)
5th rowPopular Monster
ValueCountFrequency (%)
1609
 
4.7%
the 1083
 
3.1%
rock 870
 
2.5%
jazz 522
 
1.5%
hard 454
 
1.3%
electronic 425
 
1.2%
disco 420
 
1.2%
holidays 399
 
1.2%
from 352
 
1.0%
pop 349
 
1.0%
Other values (6912) 27929
81.2%
2024-12-16T22:22:14.667773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24438
 
12.4%
e 15344
 
7.8%
o 13138
 
6.6%
a 11983
 
6.1%
i 11031
 
5.6%
n 9178
 
4.6%
r 8993
 
4.6%
t 8687
 
4.4%
s 7112
 
3.6%
l 6360
 
3.2%
Other values (296) 81359
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197623
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24438
 
12.4%
e 15344
 
7.8%
o 13138
 
6.6%
a 11983
 
6.1%
i 11031
 
5.6%
n 9178
 
4.6%
r 8993
 
4.6%
t 8687
 
4.4%
s 7112
 
3.6%
l 6360
 
3.2%
Other values (296) 81359
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197623
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24438
 
12.4%
e 15344
 
7.8%
o 13138
 
6.6%
a 11983
 
6.1%
i 11031
 
5.6%
n 9178
 
4.6%
r 8993
 
4.6%
t 8687
 
4.4%
s 7112
 
3.6%
l 6360
 
3.2%
Other values (296) 81359
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197623
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24438
 
12.4%
e 15344
 
7.8%
o 13138
 
6.6%
a 11983
 
6.1%
i 11031
 
5.6%
n 9178
 
4.6%
r 8993
 
4.6%
t 8687
 
4.4%
s 7112
 
3.6%
l 6360
 
3.2%
Other values (296) 81359
41.2%
Distinct5150
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size680.4 KiB
2024-12-16T22:22:14.925362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length61
Median length43
Mean length11.880188
Min length1

Characters and Unicode

Total characters118493
Distinct characters213
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3887 ?
Unique (%)39.0%

Sample

1st rowStephen Schwartz
2nd rowThe Weeknd
3rd rowM
4th rowPolo G
5th rowFalling In Reverse
ValueCountFrequency (%)
the 500
 
2.6%
death 333
 
1.7%
electronic 311
 
1.6%
music 271
 
1.4%
jazz 235
 
1.2%
anime 153
 
0.8%
of 150
 
0.8%
metal 130
 
0.7%
kato 127
 
0.7%
moonlight 114
 
0.6%
Other values (6405) 17024
88.0%
2024-12-16T22:22:15.316106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10018
 
8.5%
9375
 
7.9%
a 8769
 
7.4%
i 7416
 
6.3%
o 7237
 
6.1%
n 6247
 
5.3%
r 5875
 
5.0%
t 5256
 
4.4%
l 4778
 
4.0%
s 4758
 
4.0%
Other values (203) 48764
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10018
 
8.5%
9375
 
7.9%
a 8769
 
7.4%
i 7416
 
6.3%
o 7237
 
6.1%
n 6247
 
5.3%
r 5875
 
5.0%
t 5256
 
4.4%
l 4778
 
4.0%
s 4758
 
4.0%
Other values (203) 48764
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10018
 
8.5%
9375
 
7.9%
a 8769
 
7.4%
i 7416
 
6.3%
o 7237
 
6.1%
n 6247
 
5.3%
r 5875
 
5.0%
t 5256
 
4.4%
l 4778
 
4.0%
s 4758
 
4.0%
Other values (203) 48764
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10018
 
8.5%
9375
 
7.9%
a 8769
 
7.4%
i 7416
 
6.3%
o 7237
 
6.1%
n 6247
 
5.3%
r 5875
 
5.0%
t 5256
 
4.4%
l 4778
 
4.0%
s 4758
 
4.0%
Other values (203) 48764
41.2%
Distinct6276
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Memory size782.4 KiB
2024-12-16T22:22:15.585311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length217
Median length98
Mean length21.383497
Min length1

Characters and Unicode

Total characters213279
Distinct characters334
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5242 ?
Unique (%)52.6%

Sample

1st rowWicked (Original Broadway Cast Recording / Deluxe Edition)
2nd rowPopular (Music from the HBO Original Series)
3rd rowPop Muzik
4th rowDie A Legend
5th rowPopular Monster
ValueCountFrequency (%)
the 1284
 
3.6%
909
 
2.6%
anime 701
 
2.0%
indie 597
 
1.7%
jazz 591
 
1.7%
rock 589
 
1.7%
of 462
 
1.3%
christmas 405
 
1.1%
vol 403
 
1.1%
for 400
 
1.1%
Other values (6354) 29294
82.2%
2024-12-16T22:22:15.997823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25661
 
12.0%
e 17425
 
8.2%
o 13084
 
6.1%
i 12846
 
6.0%
a 11704
 
5.5%
n 10172
 
4.8%
t 9687
 
4.5%
r 9121
 
4.3%
s 8670
 
4.1%
l 7696
 
3.6%
Other values (324) 87213
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25661
 
12.0%
e 17425
 
8.2%
o 13084
 
6.1%
i 12846
 
6.0%
a 11704
 
5.5%
n 10172
 
4.8%
t 9687
 
4.5%
r 9121
 
4.3%
s 8670
 
4.1%
l 7696
 
3.6%
Other values (324) 87213
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25661
 
12.0%
e 17425
 
8.2%
o 13084
 
6.1%
i 12846
 
6.0%
a 11704
 
5.5%
n 10172
 
4.8%
t 9687
 
4.5%
r 9121
 
4.3%
s 8670
 
4.1%
l 7696
 
3.6%
Other values (324) 87213
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25661
 
12.0%
e 17425
 
8.2%
o 13084
 
6.1%
i 12846
 
6.0%
a 11704
 
5.5%
n 10172
 
4.8%
t 9687
 
4.5%
r 9121
 
4.3%
s 8670
 
4.1%
l 7696
 
3.6%
Other values (324) 87213
40.9%
Distinct2903
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
Minimum1942-01-01 00:00:00
Maximum2024-11-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-16T22:22:16.131337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:16.265400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration_ms
Real number (ℝ)

Distinct8397
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196648.14
Minimum30014
Maximum3600014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:16.392092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30014
5-th percentile79979.5
Q1138441.75
median187113
Q3234878.25
95-th percentile343323.65
Maximum3600014
Range3570000
Interquartile range (IQR)96436.5

Descriptive statistics

Standard deviation100651.6
Coefficient of variation (CV)0.51183603
Kurtosis183.47249
Mean196648.14
Median Absolute Deviation (MAD)48280
Skewness7.5140319
Sum1.9613685 × 109
Variance1.0130745 × 1010
MonotonicityNot monotonic
2024-12-16T22:22:16.523556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 31
 
0.3%
94534 26
 
0.3%
192000 13
 
0.1%
204000 13
 
0.1%
156000 12
 
0.1%
144000 12
 
0.1%
138000 12
 
0.1%
240000 11
 
0.1%
128000 11
 
0.1%
132000 10
 
0.1%
Other values (8387) 9823
98.5%
ValueCountFrequency (%)
30014 1
< 0.1%
30066 1
< 0.1%
30232 1
< 0.1%
30750 1
< 0.1%
32261 1
< 0.1%
32653 1
< 0.1%
32810 1
< 0.1%
32933 1
< 0.1%
33040 1
< 0.1%
33973 1
< 0.1%
ValueCountFrequency (%)
3600014 1
< 0.1%
2575000 1
< 0.1%
1800020 1
< 0.1%
1723500 1
< 0.1%
1510272 1
< 0.1%
1500004 1
< 0.1%
1179814 1
< 0.1%
1074720 1
< 0.1%
1046000 1
< 0.1%
990600 1
< 0.1%

genre
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size617.9 KiB
hard-rock
1055 
Electronic
1017 
pop
998 
jazz
997 
rock
996 
Other values (5)
4911 

Length

Max length11
Median length9
Mean length6.4208943
Min length3

Characters and Unicode

Total characters64042
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
hard-rock 1055
10.6%
Electronic 1017
10.2%
pop 998
10.0%
jazz 997
10.0%
rock 996
10.0%
disco 994
10.0%
holidays 994
10.0%
Indie 986
9.9%
death-metal 979
9.8%
anime 958
9.6%

Length

2024-12-16T22:22:16.646254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-16T22:22:16.810119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hard-rock 1055
10.6%
electronic 1017
10.2%
pop 998
10.0%
jazz 997
10.0%
rock 996
10.0%
disco 994
10.0%
holidays 994
10.0%
indie 986
9.9%
death-metal 979
9.8%
anime 958
9.6%

Most occurring characters

ValueCountFrequency (%)
o 6054
 
9.5%
a 5962
 
9.3%
c 5079
 
7.9%
d 5008
 
7.8%
i 4949
 
7.7%
e 4919
 
7.7%
r 4123
 
6.4%
h 3028
 
4.7%
l 2990
 
4.7%
t 2975
 
4.6%
Other values (11) 18955
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 6054
 
9.5%
a 5962
 
9.3%
c 5079
 
7.9%
d 5008
 
7.8%
i 4949
 
7.7%
e 4919
 
7.7%
r 4123
 
6.4%
h 3028
 
4.7%
l 2990
 
4.7%
t 2975
 
4.6%
Other values (11) 18955
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 6054
 
9.5%
a 5962
 
9.3%
c 5079
 
7.9%
d 5008
 
7.8%
i 4949
 
7.7%
e 4919
 
7.7%
r 4123
 
6.4%
h 3028
 
4.7%
l 2990
 
4.7%
t 2975
 
4.6%
Other values (11) 18955
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 6054
 
9.5%
a 5962
 
9.3%
c 5079
 
7.9%
d 5008
 
7.8%
i 4949
 
7.7%
e 4919
 
7.7%
r 4123
 
6.4%
h 3028
 
4.7%
l 2990
 
4.7%
t 2975
 
4.6%
Other values (11) 18955
29.6%

danceability
Real number (ℝ)

Distinct877
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57720249
Minimum0
Maximum0.985
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:16.957788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.242
Q10.45725
median0.595
Q30.714
95-th percentile0.844
Maximum0.985
Range0.985
Interquartile range (IQR)0.25675

Descriptive statistics

Standard deviation0.18174192
Coefficient of variation (CV)0.31486683
Kurtosis-0.414634
Mean0.57720249
Median Absolute Deviation (MAD)0.127
Skewness-0.38199322
Sum5757.0176
Variance0.033030125
MonotonicityNot monotonic
2024-12-16T22:22:17.093762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.772 34
 
0.3%
0.622 34
 
0.3%
0.67 33
 
0.3%
0.549 32
 
0.3%
0.705 31
 
0.3%
0.719 30
 
0.3%
0.559 30
 
0.3%
0.654 29
 
0.3%
0.588 29
 
0.3%
0.602 29
 
0.3%
Other values (867) 9663
96.9%
ValueCountFrequency (%)
0 4
< 0.1%
0.0589 1
 
< 0.1%
0.0608 1
 
< 0.1%
0.061 1
 
< 0.1%
0.0625 1
 
< 0.1%
0.0653 1
 
< 0.1%
0.0658 1
 
< 0.1%
0.0666 1
 
< 0.1%
0.0693 2
< 0.1%
0.0696 1
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.978 2
< 0.1%
0.977 1
 
< 0.1%
0.974 1
 
< 0.1%
0.969 1
 
< 0.1%
0.967 1
 
< 0.1%
0.965 1
 
< 0.1%
0.962 3
< 0.1%
0.959 1
 
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct1467
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59921966
Minimum1.92 × 10-5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:17.221736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.92 × 10-5
5-th percentile0.054095
Q10.378
median0.652
Q30.856
95-th percentile0.973
Maximum1
Range0.9999808
Interquartile range (IQR)0.478

Descriptive statistics

Standard deviation0.29160843
Coefficient of variation (CV)0.48664698
Kurtosis-0.93771848
Mean0.59921966
Median Absolute Deviation (MAD)0.227
Skewness-0.48294269
Sum5976.6169
Variance0.085035479
MonotonicityNot monotonic
2024-12-16T22:22:17.356766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.973 28
 
0.3%
0.962 28
 
0.3%
0.977 28
 
0.3%
0.988 27
 
0.3%
0.923 27
 
0.3%
0.938 27
 
0.3%
0.892 26
 
0.3%
0.943 26
 
0.3%
0.978 26
 
0.3%
0.947 25
 
0.3%
Other values (1457) 9706
97.3%
ValueCountFrequency (%)
1.92 × 10-51
< 0.1%
2.02 × 10-51
< 0.1%
2.03 × 10-51
< 0.1%
0.000272 1
< 0.1%
0.000545 1
< 0.1%
0.000884 1
< 0.1%
0.00144 1
< 0.1%
0.00146 1
< 0.1%
0.00177 1
< 0.1%
0.00225 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 9
0.1%
0.998 11
0.1%
0.997 14
0.1%
0.996 13
0.1%
0.995 19
0.2%
0.994 15
0.2%
0.993 22
0.2%
0.992 11
0.1%
0.991 22
0.2%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1763585
Minimum0
Maximum11
Zeros1198
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:17.466294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6115225
Coefficient of variation (CV)0.69769559
Kurtosis-1.308966
Mean5.1763585
Median Absolute Deviation (MAD)3
Skewness0.051938237
Sum51629
Variance13.043095
MonotonicityNot monotonic
2024-12-16T22:22:17.565364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1198
12.0%
1 1110
11.1%
7 1070
10.7%
2 1043
10.5%
9 930
9.3%
11 851
8.5%
5 797
8.0%
4 714
7.2%
6 713
7.1%
8 647
6.5%
Other values (2) 901
9.0%
ValueCountFrequency (%)
0 1198
12.0%
1 1110
11.1%
2 1043
10.5%
3 299
 
3.0%
4 714
7.2%
5 797
8.0%
6 713
7.1%
7 1070
10.7%
8 647
6.5%
9 930
9.3%
ValueCountFrequency (%)
11 851
8.5%
10 602
6.0%
9 930
9.3%
8 647
6.5%
7 1070
10.7%
6 713
7.1%
5 797
8.0%
4 714
7.2%
3 299
 
3.0%
2 1043
10.5%

loudness
Real number (ℝ)

High correlation 

Distinct7393
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.50441
Minimum-46.986
Maximum0.642
Zeros0
Zeros (%)0.0%
Negative9968
Negative (%)99.9%
Memory size78.1 KiB
2024-12-16T22:22:17.864498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-46.986
5-th percentile-25.26835
Q1-13.14175
median-8.3995
Q3-5.73
95-th percentile-3.45925
Maximum0.642
Range47.628
Interquartile range (IQR)7.41175

Descriptive statistics

Standard deviation6.9310808
Coefficient of variation (CV)-0.6598258
Kurtosis2.9764394
Mean-10.50441
Median Absolute Deviation (MAD)3.2395
Skewness-1.6326722
Sum-104770.99
Variance48.039881
MonotonicityNot monotonic
2024-12-16T22:22:17.988257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.531 8
 
0.1%
-5.327 7
 
0.1%
-4.701 7
 
0.1%
-6.502 7
 
0.1%
-5.423 7
 
0.1%
-8.452 7
 
0.1%
-5.411 6
 
0.1%
-6.441 6
 
0.1%
-8.698 6
 
0.1%
-7.743 6
 
0.1%
Other values (7383) 9907
99.3%
ValueCountFrequency (%)
-46.986 1
< 0.1%
-46.979 1
< 0.1%
-44.85 1
< 0.1%
-43.484 1
< 0.1%
-43.082 1
< 0.1%
-42.924 1
< 0.1%
-42.717 1
< 0.1%
-42.686 1
< 0.1%
-42.593 1
< 0.1%
-42.38 1
< 0.1%
ValueCountFrequency (%)
0.642 1
< 0.1%
0.58 1
< 0.1%
0.461 1
< 0.1%
0.421 1
< 0.1%
0.03 1
< 0.1%
0.024 1
< 0.1%
-0.002 1
< 0.1%
-0.016 1
< 0.1%
-0.436 1
< 0.1%
-0.463 1
< 0.1%

tempo
Real number (ℝ)

High correlation 

Distinct8268
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.37747
Minimum0
Maximum220.01
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:18.110816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.5042
Q199.06275
median120.982
Q3140.016
95-th percentile174.9164
Maximum220.01
Range220.01
Interquartile range (IQR)40.95325

Descriptive statistics

Standard deviation30.005565
Coefficient of variation (CV)0.24720868
Kurtosis-0.24904331
Mean121.37747
Median Absolute Deviation (MAD)20.9545
Skewness0.25052304
Sum1210618.9
Variance900.33393
MonotonicityNot monotonic
2024-12-16T22:22:18.226217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.996 9
 
0.1%
127.987 8
 
0.1%
119.987 8
 
0.1%
120.023 8
 
0.1%
120.006 7
 
0.1%
130.014 7
 
0.1%
120.012 7
 
0.1%
120 7
 
0.1%
119.993 6
 
0.1%
127.968 6
 
0.1%
Other values (8258) 9901
99.3%
ValueCountFrequency (%)
0 4
< 0.1%
36.015 1
 
< 0.1%
36.336 1
 
< 0.1%
39.72 1
 
< 0.1%
39.769 1
 
< 0.1%
42.78 1
 
< 0.1%
43.84 1
 
< 0.1%
44.07 1
 
< 0.1%
45.466 1
 
< 0.1%
47.523 1
 
< 0.1%
ValueCountFrequency (%)
220.01 1
< 0.1%
216.185 1
< 0.1%
215.796 1
< 0.1%
213.055 1
< 0.1%
212.192 2
< 0.1%
212.108 1
< 0.1%
210.983 1
< 0.1%
210.222 1
< 0.1%
209.964 1
< 0.1%
209.364 1
< 0.1%

time_signature
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size565.1 KiB
4
8922 
3
 
815
5
 
161
1
 
72
0
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9974
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

Length

2024-12-16T22:22:18.349393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-16T22:22:18.451654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 8922
89.5%
3 815
 
8.2%
5 161
 
1.6%
1 72
 
0.7%
0 4
 
< 0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct2798
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35490101
Minimum0
Maximum0.998
Zeros2102
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:18.563297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.9525 × 10-6
median0.03405
Q30.846
95-th percentile0.947
Maximum0.998
Range0.998
Interquartile range (IQR)0.84599505

Descriptive statistics

Standard deviation0.40815394
Coefficient of variation (CV)1.1500501
Kurtosis-1.6718531
Mean0.35490101
Median Absolute Deviation (MAD)0.03405
Skewness0.43918755
Sum3539.7827
Variance0.16658964
MonotonicityNot monotonic
2024-12-16T22:22:18.689355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2102
 
21.1%
0.924 35
 
0.4%
0.883 30
 
0.3%
0.944 30
 
0.3%
0.917 29
 
0.3%
0.945 28
 
0.3%
0.916 28
 
0.3%
0.902 28
 
0.3%
0.9 27
 
0.3%
0.915 27
 
0.3%
Other values (2788) 7610
76.3%
ValueCountFrequency (%)
0 2102
21.1%
1 × 10-62
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-64
 
< 0.1%
1.03 × 10-63
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-61
 
< 0.1%
1.07 × 10-63
 
< 0.1%
1.08 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.998 2
< 0.1%
0.995 1
 
< 0.1%
0.992 4
< 0.1%
0.991 2
< 0.1%
0.989 1
 
< 0.1%
0.988 1
 
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%
0.985 2
< 0.1%
0.984 2
< 0.1%

liveness
Real number (ℝ)

Distinct1312
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19276613
Minimum0.0114
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:18.814515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0114
5-th percentile0.0617
Q10.0983
median0.122
Q30.242
95-th percentile0.53635
Maximum0.991
Range0.9796
Interquartile range (IQR)0.1437

Descriptive statistics

Standard deviation0.16152324
Coefficient of variation (CV)0.83792335
Kurtosis5.8223742
Mean0.19276613
Median Absolute Deviation (MAD)0.0402
Skewness2.2655299
Sum1922.6494
Variance0.026089758
MonotonicityNot monotonic
2024-12-16T22:22:18.938088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 150
 
1.5%
0.108 147
 
1.5%
0.111 147
 
1.5%
0.112 146
 
1.5%
0.109 140
 
1.4%
0.106 133
 
1.3%
0.107 122
 
1.2%
0.104 120
 
1.2%
0.105 115
 
1.2%
0.102 110
 
1.1%
Other values (1302) 8644
86.7%
ValueCountFrequency (%)
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0146 1
< 0.1%
0.016 2
< 0.1%
0.0161 1
< 0.1%
0.0182 1
< 0.1%
0.0211 1
< 0.1%
0.0212 2
< 0.1%
0.0216 1
< 0.1%
0.0227 1
< 0.1%
ValueCountFrequency (%)
0.991 1
 
< 0.1%
0.989 1
 
< 0.1%
0.98 2
< 0.1%
0.979 1
 
< 0.1%
0.975 2
< 0.1%
0.972 1
 
< 0.1%
0.971 1
 
< 0.1%
0.969 3
< 0.1%
0.966 1
 
< 0.1%
0.965 2
< 0.1%

valence
Real number (ℝ)

Distinct1324
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46544624
Minimum0
Maximum1
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size78.1 KiB
2024-12-16T22:22:19.062707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0685
Q10.246
median0.452
Q30.67275
95-th percentile0.912
Maximum1
Range1
Interquartile range (IQR)0.42675

Descriptive statistics

Standard deviation0.26106665
Coefficient of variation (CV)0.56089539
Kurtosis-1.0291006
Mean0.46544624
Median Absolute Deviation (MAD)0.214
Skewness0.16397973
Sum4642.3608
Variance0.068155798
MonotonicityNot monotonic
2024-12-16T22:22:19.201253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.964 25
 
0.3%
0.276 24
 
0.2%
0.962 22
 
0.2%
0.961 22
 
0.2%
0.394 22
 
0.2%
0.175 22
 
0.2%
0.23 22
 
0.2%
0.671 22
 
0.2%
0.612 22
 
0.2%
0.156 21
 
0.2%
Other values (1314) 9750
97.8%
ValueCountFrequency (%)
0 9
0.1%
1 × 10-511
0.1%
0.00403 1
 
< 0.1%
0.0058 1
 
< 0.1%
0.0126 1
 
< 0.1%
0.0218 1
 
< 0.1%
0.0222 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.0234 1
 
< 0.1%
0.0236 2
 
< 0.1%
ValueCountFrequency (%)
1 3
< 0.1%
0.989 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.981 3
< 0.1%
0.98 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 1
 
< 0.1%

Interactions

2024-12-16T22:22:11.759703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:03.882524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.077481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.016079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.924401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.849487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.762398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.647346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.637802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.869905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.217557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.186893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.125176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.030993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.960623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.871650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.757699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.752398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.975451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.329384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.295002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.226370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.133578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.066189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.973233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.867752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.851979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.075007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.439933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.399547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.326099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.231556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.163769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.072483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.971937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.960505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.174606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.545470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.517114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.425704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.337146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.261342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.167703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.072956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.058559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.276110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.654152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.618432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.523385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.435222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.355342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.259254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.200521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.152129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.371626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.756745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.715017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.620995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.528794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.448902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.350359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.299121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.268241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.476675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.865267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.815335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.724312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.631361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.566981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.447989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.410667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.556541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:12.580196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:04.969772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:05.916000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:06.824985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:07.739927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:08.666232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:09.547544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:10.539194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-12-16T22:22:11.653136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-12-16T22:22:19.310897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
danceabilityduration_msenergygenreinstrumentalnesskeylivenessloudnesstempotime_signaturevalence
danceability1.000-0.141-0.1430.176-0.1340.039-0.157-0.022-0.0220.2070.421
duration_ms-0.1411.0000.3620.044-0.1900.0120.0510.3430.0260.000-0.010
energy-0.1430.3621.0000.247-0.2620.0410.2150.7960.2130.1270.167
genre0.1760.0440.2471.0000.1730.0390.0680.2050.0960.0660.111
instrumentalness-0.134-0.190-0.2620.1731.000-0.015-0.171-0.495-0.0790.058-0.295
key0.0390.0120.0410.039-0.0151.000-0.0000.0230.0070.0160.038
liveness-0.1570.0510.2150.068-0.171-0.0001.0000.1890.0260.0290.003
loudness-0.0220.3430.7960.205-0.4950.0230.1891.0000.1690.1150.230
tempo-0.0220.0260.2130.096-0.0790.0070.0260.1691.0000.5050.103
time_signature0.2070.0000.1270.0660.0580.0160.0290.1150.5051.0000.086
valence0.421-0.0100.1670.111-0.2950.0380.0030.2300.1030.0861.000

Missing values

2024-12-16T22:22:12.747711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-16T22:22:12.983021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

song_idtitleartist_namealbum_namerelease_dateduration_msgenredanceabilityenergykeyloudnesstempotime_signatureinstrumentalnesslivenessvalence
057Pk2GU0ABFYBbbcgYxqkiPopular - From "Wicked" Original Broadway Cast Recording/2003Stephen SchwartzWicked (Original Broadway Cast Recording / Deluxe Edition)2013-01-01224386pop0.7110.2280-12.85088.71440.0000000.12200.548
16WzRpISELf3YglGAh7TXcGPopular (with Playboi Carti & Madonna) - From The Idol Vol. 1 (Music from the HBO Original Series)The WeekndPopular (Music from the HBO Original Series)2023-06-02215466pop0.8540.6741-6.23099.02240.0000850.50900.848
26z3HAUZpAyJ0ctsbAwAiw3Pop MuzikMPop Muzik2022-06-03200682pop0.9350.8331-7.493108.91440.0000160.07920.967
36uFn47ACjqYkc0jADwEdj1Pop Out (feat. Lil Tjay)Polo GDie A Legend2019-06-07166560pop0.7720.6391-7.119168.11240.0000000.06980.261
44GssB27iJeqmfGxS94TfijPopular MonsterFalling In ReversePopular Monster2019-11-20220537pop0.4610.8973-3.982165.10740.0000000.09650.358
54myFsmx2v6znDOJfn3IkbDPopular MonsterFalling In ReversePopular Monster2024-08-16220537pop0.4610.8983-3.982165.07940.0000000.09630.353
66AviHKu3ydzAePBmzEi62vPopular SongMIKAYours Truly2013-01-01200213pop0.6660.8108-4.57499.02340.0000000.06130.811
779s5XnCN4TJKTVMSmOx8EpDiorPop SmokeMeet The Woo2019-07-26216386pop0.5480.8057-5.732142.09640.0004050.40800.649
83wn8nOygkHLUQ9dlXM1rKWPoppinYeat2 Alivë2022-02-18167186pop0.7050.6137-7.44174.52140.0000070.19800.259
90widrZ6KVNuIPhbM1rWPDRPop StarCoco & Clair ClairSexy2022-11-04184222pop0.8910.6700-7.256128.04640.0027300.11000.938
song_idtitleartist_namealbum_namerelease_dateduration_msgenredanceabilityenergykeyloudnesstempotime_signatureinstrumentalnesslivenessvalence
9964260h0j3EoZYsOnnlEzY6voSpider Dance Anime OpeningThai McGrathSpider Dance Anime Opening2024-04-13187431anime0.6230.97001-3.658105.04740.0000.3130.7320
99651u3jNRvccJexSiHvEwRkZmKunoAnime MoonlightDeath Note2024-06-19109367anime0.7650.27407-13.02879.00640.9240.1580.3350
99661wCn6xWvFFtY86zcw9LIX4Moonlight in KyotoAnime MoonlightMoonlight Destination2024-08-02111428anime0.6830.30600-11.312140.10340.7910.1260.0565
99674WefBsWnKnKsx1MxThJjOCAkane's Sayoonara CallChillHoopAnime Moonlight Vol. 22024-07-1299230anime0.6700.39604-14.666156.00750.9290.1710.5230
99681g1hJuLAssI47Gb5V2yBCTApollon Blue (From "Sakamichi No Apollon") - Piano VersionAtsumi MikoAnime Piano Sleep2023-06-1693857anime0.4370.02123-26.73570.85040.9410.1040.3070
99694FmUrmRianI41BlO2RYhewShe Was Here, Alone (From "Erased")LucasPianoRoomAnime Piano, Vol. 22020-04-02140945anime0.4420.05934-19.09362.98730.9510.1550.1080
997045YTUUPFSm6GS6LsddwRmjBeautiful World (From "Darling in the FranXX")Fonzi MAnime Piano Music Collection in Spring, Vol. 22018-04-2092282anime0.5170.36404-16.545131.95840.9640.2300.7260
99711DXSEjzzL5x9TybrYE2A3KEminent (Soundtrack from the Anime "Black Butler Book" of the Atlantic)RolelushAnime and Game on Piano2018-12-0380039anime0.3230.16902-23.435117.08340.8440.2120.0954
99721E65lXfTCueQizuL4z8knxA Silent Voice- Lit var - Koe no KatachiOtaku MuiscOpening Triste ,Vol.12022-04-11150932anime0.3860.04487-19.89288.12830.9580.1030.0729
99731fDkKDjiAgVn3uQPpvdAPxNabiki's Letter to PeaceChillHoopAnime Moonlight Vol. 22024-07-1299000anime0.7060.19209-18.03179.99040.1680.3070.3240